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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 641–650 of 1808 papers

Title	Date	Tasks	Status	Hype
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks	Mar 15, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation	Mar 13, 2024	Adversarial AttackConditional Image Generation	—Unverified	0
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics	Mar 9, 2024	Adversarial Attack	CodeCode Available	0
Hard-label based Small Query Black-box Adversarial Attack	Mar 9, 2024	Adversarial Attack	CodeCode Available	0
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm	Mar 8, 2024	Adversarial AttackAutonomous Navigation	—Unverified	0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors	Mar 6, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters	Mar 2, 2024	Adversarial Attack	—Unverified	0
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey	Mar 1, 2024	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification	Feb 29, 2024	Adversarial AttackClassification	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified